Merge pull request #51 from deezer/matrosovarecsys

Matrosovarecsys

Author: mmoussallam

_posts/2024-09-04-recsys-matrosova _posts/2024-09-04-recsys-matrosova.md_posts/2024-09-04-recsys-matrosova renamed to _posts/2024-09-04-recsys-matrosova.md

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+---
+layout: post
+title: Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data
+date: 2024-09-04 13:13:13 +0200
+category: Publication
+readtime: 6
+author: kmatrosova
+projects:
+ - RECORDS
+people:
+ - kmatrosova
+ - lmarey
+ - gsalhagalvan
+ - mmoussallam
+
+publication_type: conference
+publication_title: "Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data"
+publication_year: 2024
+publication_authors: K. Matrosova, L. Marey, G. Salha-Galvan, T. Louail, O. Bodini, M. Moussallam
+publication_conference: RecSys
+publication_code: "https://github.com/kmatrosova/LocalMusicRecSys2024"
+publication_preprint: "https://arxiv.org/pdf/2408.16430"
+domains: 
+ - RECSYS
+---
+
+This paper examines the influence of recommender systems on local music representation, discussing prior findings from an empirical study on the LFM-2b public dataset. This prior study argued that different recommender systems exhibit algorithmic biases shifting music consumption either towards or against local content.
+However, LFM-2b users do not reflect the diverse audience of music streaming services. To assess the robustness of this study’s conclusions, we conduct a comparative analysis using proprietary listening data from a global music streaming service, which we
+publicly release alongside this paper. 
+We observe significant differences in local music consumption patterns between our dataset and LFM-2b, suggesting that caution should be exercised when drawing conclusions on local music based solely on LFM-2b. 
+Moreover, we show that the algorithmic biases exhibited in the original work vary in our dataset, and that several unexplored model parameters can significantly influence these biases and affect the study’s conclusion
+on both datasets. Finally, we discuss the complexity of accurately labeling local music, emphasizing the risk of misleading conclusions due to unreliable, biased, or incomplete labels. 
+To encourage further research and ensure reproducibility, we have publicly shared our dataset and code.
+
+This paper has been accepted at the 18th ACM Conference on Recommender Systems (RecSys 2024) in the Reproducibility Track.